Often we write programs in Python for convenience, not for speed. When work becomes elevated to High Performance Computing (HPC) environments, speed once again becomes a concern. Cython is an extension of Python which allows functions to be compiled as C (or C++) and recover the significant performance trade-offs of Python. Cython achieves this by supporting calling C functions, declaring of type information, as well as providing access to C++ STL functionality. Popular packages and libraries that take advantage of Cython include: TensorFlow, OpenCV, NumPy, Pandas, and more. This webinar will cover a basic introduction to Cython, a demo translating vanilla Python into Cython, followed by a short demo of how to run Cython in our own Compute Canada HPC environments. Experience with Python will be expected, while familiarity with C/C++ and Jupyter notebooks will be helpful. Webinar material and code will be made available on GitHub for reference.
This repository contains the slides and jupyter notebooks for my general interest webinar.
A completed version of the live demo can be found in reference.ipynb.
Inside of the cc_use directory are instructions for how to compile and use Cython on the Compute Canada systems outside of a notebook environment.